首页|New Machine Learning Study Findings Recently Were Reported by Researchers at Nan jing University of Information Science and Technology (NUIST) (Real-time In Situ Detection of Petroleum Hydrocarbon Pollution In Soils Via a Novel Optical ...)
New Machine Learning Study Findings Recently Were Reported by Researchers at Nan jing University of Information Science and Technology (NUIST) (Real-time In Situ Detection of Petroleum Hydrocarbon Pollution In Soils Via a Novel Optical ...)
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A new study on Machine Learning is now available. According to news reporting out of Nanjing, People's Republic of Chi na, by NewsRx editors, research stated, "Petroleum hydrocarbon (PHC) contaminati on in soils is considered one of the most serious problems currently, of which t he detection and identification is a fairly significant but challenging work. Co nventional methods to do such work usually need complex sample pretreatment, con sume much time and fail to do the in-situ detection." Funders for this research include National Natural Science Foundation of China ( NSFC), Qinglan Project of Jiangsu Province. Our news journalists obtained a quote from the research from the Nanjing Univers ity of Information Science and Technology (NUIST), "This paper set out to create a novel systematic methodology to realize the goals accurately and efficiently. Based on laser-induced breakdown spectroscopy (LIBS) and selfimproved machine learning methods, the innovative methodology only uses extremely simple devices to do the real-time in situ detection and identification work and even realize t he quantitative analysis of pollution level accurately. In the study, clean soil s mixed with petroleum were served as polluted samples, clean soils to be the bl ank group for comparison. Based on the elemental information from the spectra ob tained by LIBS, machine learning methods were improved and helped optimized the algorithm to identify the PHC polluted soil samples for the first time. Furtherm ore, a novel model was designed to perform the quantitative analysis of the conc entration of PHC pollution in soils, which can be applied to detect the degree o f PHC contamination in soils accurately. Finally, the harmful volatile component of the PHC polluted soils was also successfully and identified despite its extr emely minimal content in the air."
NanjingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesHydrocarbonsMachine LearningOrganic Chemi calsNanjing University of Information Science and Technology (NUIST)
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